Throughput Optimization in Mobile Backbone Networks by Craparo, Emily M., How, Jonathan P., Modiano, Eytan H.

“…This paper presents a theoretical performance guarantee for the approximation algorithm and also demonstrates its empirical performance. …”
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A continuous analogue of the tensor-train decomposition by Gorodetsky, Alex, Karaman, Sertac, Marzouk, Youssef M.

“…We develop new approximation algorithms and data structures for representing and computing with multivariate functions using the functional tensor-train (FT), a continuous extension of the tensor-train (TT) decomposition. …”
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An asymptotically optimal algorithm for pickup and delivery problems by Pavone, Marco, Frazzoli, Emilio, Treleaven, Kyle Ballantyne

“…The SCP is NP-Hard and the best know approximation algorithm only provides a 9/5 approximation ratio. …”
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Asymptotically Optimal Algorithms for One-to-One Pickup and Delivery Problems With Applications to Transportation Systems by Pavone, Marco, Frazzoli, Emilio, Treleaven, Kyle Ballantyne

“…The SCP is NP-Hard and the best known approximation algorithm only provides a 9/5 approximation ratio. …”
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Construction and Maintenance of Wireless Mobile Backbone Networks by Srinivas, Anand, Zussman, Gil, Modiano, Eytan H.

“…Then, we focus on the two subproblems and present a number of distributed approximation algorithms that maintain a solution to the GDC problem under mobility. …”
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Robust design of spectrum-sharing networks by Lee, Hyang-Won, Liang, Qingkai, Modiano, Eytan H

“…We characterize the structure of the optimal assignment and develop bi-criteria approximation algorithms. Moreover, we investigate the scaling of the recovery capacity as the network size becomes large. …”
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Reliability in layered networks with random link failures by Lee, Kayi, Lee, Hyang-Won, Modiano, Eytan H.

“…Using random sampling techniques, we develop polynomial time approximation algorithms for the failure polynomial. Our approach gives an approximate expression for reliability as a function of the link failure probability, eliminating the need to resample for different values of the failure probability. …”
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Survivability in time-varying networks by Liang, Qingkai, Modiano, Eytan H

“…Then we analyze the complexity of computing the proposed metrics and develop approximation algorithms. Finally, we conduct trace-driven simulations to demonstrate the application of our survivability framework in the robust design of a real-world bus communication network.…”
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Intention-Aware Motion Planning by Bandyopadhyay, Tirthankar, Won, Kok Sung, Hsu, David, Lee, Wee Sun, Frazzoli, Emilio, Rus, Daniela L

“…By leveraging the latest advances in POMDP/MOMDP approximation algorithms, we can construct and solve moderately complex models for interesting robotic tasks. …”
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Survivable paths in multilayer networks by Parandehgheibi, Marzieh, Lee, Hyang-Won, Modiano, Eytan H.

“…We formulate the problems as Integer Linear Programs (ILPs), and use these formulations to develop heuristics and approximation algorithms.…”
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Simulation-based optimal Bayesian experimental design for nonlinear systems by Huan, Xun, Marzouk, Youssef M.

“…Polynomial chaos approximations and a two-stage Monte Carlo sampling method are used to evaluate the expected information gain. Stochastic approximation algorithms are then used to make optimization feasible in computationally intensive and high-dimensional settings. …”
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Joint Feature Selection and Time Optimal Path Parametrization for High Speed Vision-Aided Navigation by Spasojevic, Igor, Murali, Varun, Karaman, Sertac

“…We provide two efficient approximation algorithms for addressing the aforementioned problem. …”
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Mercer Kernels and Integrated Variance Experimental Design: Connections Between Gaussian Process Regression and Polynomial Approximation by Gorodetsky, Alex Arkady, Marzouk, Youssef M

“…This paper examines experimental design procedures used to develop surrogates of computational models, exploring the interplay between experimental designs and approximation algorithms. We focus on two widely used approximation approaches, Gaussian process (GP) regression and nonintrusive polynomial approximation. …”
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